Accelerating Foreign-Key Joins using Asymmetric Memory Channels

Indexed Foreign-Key Joins expose a very asymmetric access pattern: the Foreign-Key Index is sequentially scanned whilst the Primary-Key table is target of many quasi-random lookups which is the dominant cost factor. To reduce the costs of the random lookups the fact-table can be (re-) partitioned at runtime to increase access locality on the dimension table, and thus limit the random memory access to inside the CPU's cache. However, this is very hard to optimize and the performance impact on recent architectures is limited because the partitioning costs consume most of the achievable join improvement. GPGPUs on the other hand have an architecture that is well suited for this operation: a relatively slow connection to the large system memory and a very fast connection to the smaller internal device memory. We show how to accelerate Foreign-Key Joins by executing the random table lookups on the GPU's VRAM while sequentially streaming the Foreign- Key-Index through the PCI-E Bus. We also experimentally study the memory access costs on GPU and CPU to provide estimations of the benefit of this technique

Benchmarking adaptive indexing

Ideally, realizing the best physical design for the current and all subsequent workloads would impact neither performance nor storage usage. In reality, workloads and datasets can change dramatically over time and index creation impacts the performance of concurrent user and system activity. We propose a framework that evaluates the key premise of adaptive indexing --- a new indexing paradigm where index creation and re-organization take place automatically and incrementally, as a side-effect of query execution. We focus on how the incremental costs and benefits of dynamic reorganization are distributed across the workload's lifetime. We believe measuring the costs and utility of the stages of adaptation are relevant metrics for evaluating new query processing paradigms and comparing them to traditional approaches

X-Device Query Processing by Bitwise Distribution

The diversity of hardware components within a single system calls for strategies for efficient cross-device data processing. For exam- ple, existing approaches to CPU/GPU co-processing distribute individual relational operators to the “most appropriate” device. While pleasantly simple, this strategy has a number of problems: it may leave the “inappropriate” devices idle while overloading the “appropriate” device and putting a high pressure on the PCI bus. To address these issues we distribute data among the devices by par- tially decomposing relations at the granularity of individual bits. Each of the resulting bit-partitions is stored and processed on one of the available devices. Using this strategy, we implemented a processor for spatial range queries that makes efficient use of all available devices. The performance gains achieved indicate that bitwise distribution makes a good cross-device processing strategy

Quality predictors of abdominal fetal electrocardiography recording in antenatal ambulatory and bedside settings

Background: Fetal electrocardiography using an abdominal monitor (Monica AN24™) could increase the diagnostic use of fetal heart rate (fHR) variability measurements. However, signal quality may depend on factors such as maternal physical activity, posture, and bedside versus ambulatory setting. Methods: Sixty-three healthy women wore the monitor at home and 42 women during a hospital stay. All women underwent a posture experiment, and all home and 13 hospital participants wore the monitor during daytime and nighttime. The success rate (SR) of fHR detection was analyzed in relation to maternal physical activity, posture, daytime versus nighttime, and other maternal and fetal predictors. Results: Ambulatorily, the SR was 86.8% for nighttime and 40.2% for daytime. The low daytime SR was largely due to effects of maternal physical activity and posture. The in-hospital SR was lower during nighttime (71.1%) and similar during daytime (43.3%). SR was related to gestational age, but not affected by pre-pregnancy and current body mass index or fetal growth restriction. Conclusions: The success of beat-to-beat fHR detection strongly depends on the home/hospital setting and predictors such as time of recording, activity levels, and maternal posture. Its clinical utility may be limited in periods of unsupervised recording with physical activity or posture shifts

Instant-on scientific data warehouses: Lazy ETL for data-intensive research

In the dawning era of data intensive research, scientific discovery deploys data analysis techniques similar to those that drive business intelligence. Similar to classical Extract, Transform and Load (ETL) processes, data is loaded entirely from external data sources (repositories) into a scientific data warehouse before it can be analyzed. This process is both, time and resource intensive and may not be entirely necessary if only a subset of the data is of interest to a particular user. To overcome this problem, we propose a novel technique to lower the costs for data loading: Lazy ETL. Data is extracted and loaded transparently on-the-fly only for the required data items. Extensive experiments demonstrate the significant reduction of the time from source data availability to query answer compared to state-of-the-art solutions. In addition to reducing the costs for bootstrapping a scientific data warehouse, our approach also reduces the costs for loading new incoming data

Scalable Generation of Synthetic GPS Traces with Real-life Data Characteristics

Database benchmarking is most valuable if real-life data and workloads are available. However, real-life data (and workloads) are often not publicly available due to IPR constraints or privacy concerns. And even if available, they are often limited regarding scalability and variability of data characteristics. On the oth

Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)

Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I/O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I/O model such as the one implemented in PostgreSQL may make query response time predictions that are up to 2X slower than the optimal multi-join query plan over memory-resident data. This paper introduces a memory I/O cost model to identify good evaluation strategies for complex query plans with multiple hash-based equi-joins over memory-resident data. The proposed cost model is carefully validated for accuracy using three different systems, including an Amazon EC2 instance, to control for hardware-specific differences. Prior work in parallel query evaluation has advocated right-deep and bushy trees for multi-join queries due to their greater parallelization and pipelining potential. A surprising finding is that the conventional wisdom from shared-nothing disk-based systems does not directly apply to the modern shared-everything memory hierarchy. As corroborated by our model, the performance gap between the optimal left-deep and right-deep query plan can grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in SoCC'1

Transactional support for adaptive indexing

Adaptive indexing initializes and optimizes indexes incrementally, as a side effect of query processing. The goal is to achieve the benefits of indexes while hiding or minimizing the costs of index creation. However, index-optimizing side effects seem to turn read-only queries into update transactions that might, for example, create lock contention. This paper studies concurrency contr

Замена электродвигателя ПЭН турбоприводом на Кемеровской ТЭЦ

В данной работе рассматривается возможность замены электродвигателя ПЭН турбоприводом на Кемеровской ТЭЦ, с установкой турбопривода на существующий фундамент. Целью работы является оценка возможности увеличения отпуска электроэнергии от станции в результате уменьшения затрат на собственные нужды и повышение маневренности ТЭЦ.In this paper we consider the possibility of replacing the turbine drive motor PEN to Kemerovo CHP , with the installation of turbine drive on the existing foundation. The aim is to assess the possibility of increasing the supply of electric power from the plant by reducing the costs of their own needs and improving maneuverability CHP

A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics (Extended Version)

There has been significant amount of excitement and recent work on GPU-based database systems. Previous work has claimed that these systems can perform orders of magnitude better than CPU-based database systems on analytical workloads such as those found in decision support and business intelligence applications. A hardware expert would view these claims with suspicion. Given the general notion that database operators are memory-bandwidth bound, one would expect the maximum gain to be roughly equal to the ratio of the memory bandwidth of GPU to that of CPU. In this paper, we adopt a model-based approach to understand when and why the performance gains of running queries on GPUs vs on CPUs vary from the bandwidth ratio (which is roughly 16x on modern hardware). We propose Crystal, a library of parallel routines that can be combined together to run full SQL queries on a GPU with minimal materialization overhead. We implement individual query operators to show that while the speedups for selection, projection, and sorts are near the bandwidth ratio, joins achieve less speedup due to differences in hardware capabilities. Interestingly, we show on a popular analytical workload that full query performance gain from running on GPU exceeds the bandwidth ratio despite individual operators having speedup less than bandwidth ratio, as a result of limitations of vectorizing chained operators on CPUs, resulting in a 25x speedup for GPUs over CPUs on the benchmark